Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification

Mohamed Barakat A Gibril,Bahareh Kalantar,Abdallah Shanableh,Naonori Ueda,Helmi Z M Shafri,Vahideh Saeidi,Shattri Mansor,Rami Al-Ruzouq

doi:10.3390/rs12071081

Mohamed Barakat A Gibril, Bahareh Kalantar + Show 6 more

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https://doi.org/10.3390/rs12071081

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Abstract

Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.

Highlights

Land use/land cover (LULC) maps play an indispensable part in gaining comprehensive insights into coupled human–environment systems, socioecological concerns, resource inventories, ecosystem management, planning activities, change monitoring, emergency response, and decision making
The initial stage of classification in this study is to find the optimum level for extracting each class, which can be achieved using machine learning (ML) models, followed by a class-specific accuracy measure
Considering that segmenting UHSR unmanned aerial vehicle (UAV)-based images of a heterogeneous and complex urban landscape is a challenging task in Geographic object-based image analysis (GEOBIA), the selection of the optimum scale parameter (SP)(s) is an imperative step to ensure that different landscapes are well delineated at different scales

Summary

Introduction

Land use/land cover (LULC) maps play an indispensable part in gaining comprehensive insights into coupled human–environment systems, socioecological concerns, resource inventories, ecosystem management, planning activities, change monitoring, emergency response, and decision making. Remote sensing technologies have been extensively used to retrieve LULC information using comprehensive options of platforms and sensors with versatile spatial, spectral, and temporal resolutions. Nowadays, unmanned aerial vehicles (UAVs) are used to collect remotely sensed data in a cost-effective manner at low altitudes below the cloud cover with ultrahigh spatial (UHSR) spectral and temporal resolutions. These advantages make the UAV system a powerful tool that can be used to fulfil the rapid monitoring and assessment during a natural disaster and real-time monitoring applications [6,7]. A plethora of studies have successfully used UAV platforms to acquire remotely sensed data for LULC applications [7,8,9,10,11,12,13]

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